Overview

Dataset statistics

Number of variables14
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.1 KiB
Average record size in memory113.3 B

Variable types

Categorical3
Numeric11

Alerts

title has a high cardinality: 100 distinct values High cardinality
artist has a high cardinality: 64 distinct values High cardinality
energy is highly correlated with loudness.dB and 1 other fieldsHigh correlation
loudness.dB is highly correlated with energyHigh correlation
acousticness is highly correlated with energyHigh correlation
year is highly correlated with lengthHigh correlation
energy is highly correlated with loudness.dB and 1 other fieldsHigh correlation
loudness.dB is highly correlated with energy and 1 other fieldsHigh correlation
length is highly correlated with yearHigh correlation
acousticness is highly correlated with energy and 1 other fieldsHigh correlation
top genre is highly correlated with artist and 1 other fieldsHigh correlation
artist is highly correlated with top genre and 1 other fieldsHigh correlation
title is highly correlated with top genre and 1 other fieldsHigh correlation
title is highly correlated with artist and 12 other fieldsHigh correlation
artist is highly correlated with title and 10 other fieldsHigh correlation
top genre is highly correlated with title and 8 other fieldsHigh correlation
year is highly correlated with title and 2 other fieldsHigh correlation
beats.per.minute is highly correlated with title and 3 other fieldsHigh correlation
energy is highly correlated with title and 5 other fieldsHigh correlation
danceability is highly correlated with title and 4 other fieldsHigh correlation
loudness.dB is highly correlated with title and 3 other fieldsHigh correlation
liveness is highly correlated with title and 2 other fieldsHigh correlation
valance is highly correlated with title and 1 other fieldsHigh correlation
length is highly correlated with title and 2 other fieldsHigh correlation
acousticness is highly correlated with title and 3 other fieldsHigh correlation
speechiness is highly correlated with title and 4 other fieldsHigh correlation
popularity is highly correlated with title and 1 other fieldsHigh correlation
title is uniformly distributed Uniform
title has unique values Unique
acousticness has 8 (8.0%) zeros Zeros

Reproduction

Analysis started2021-10-07 17:09:28.137389
Analysis finished2021-10-07 17:10:08.982441
Duration40.85 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

title
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
Blinding Lights
 
1
One Kiss (with Dua Lipa)
 
1
7 Years
 
1
Don't Let Me Down
 
1
Sorry
 
1
Other values (95)
95 

Length

Max length62
Median length13
Mean length16.01
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowBlinding Lights
2nd rowWatermelon Sugar
3rd rowMood (feat. iann dior)
4th rowSomeone You Loved
5th rowPerfect

Common Values

ValueCountFrequency (%)
Blinding Lights1
 
1.0%
One Kiss (with Dua Lipa)1
 
1.0%
7 Years1
 
1.0%
Don't Let Me Down1
 
1.0%
Sorry1
 
1.0%
New Rules1
 
1.0%
Attention1
 
1.0%
I'm Yours1
 
1.0%
Old Town Road - Remix1
 
1.0%
Youngblood1
 
1.0%
Other values (90)90
90.0%

Length

2021-10-07T22:40:09.224735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
11
 
3.8%
feat10
 
3.4%
you8
 
2.8%
me8
 
2.8%
with6
 
2.1%
i6
 
2.1%
the5
 
1.7%
like5
 
1.7%
remix5
 
1.7%
don't4
 
1.4%
Other values (198)222
76.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

artist
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct64
Distinct (%)64.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
Post Malone
 
7
Ed Sheeran
 
5
The Weeknd
 
4
Imagine Dragons
 
4
Shawn Mendes
 
3
Other values (59)
77 

Length

Max length23
Median length11
Mean length10.85
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)45.0%

Sample

1st rowThe Weeknd
2nd rowHarry Styles
3rd row24kGoldn
4th rowLewis Capaldi
5th rowEd Sheeran

Common Values

ValueCountFrequency (%)
Post Malone7
 
7.0%
Ed Sheeran5
 
5.0%
The Weeknd4
 
4.0%
Imagine Dragons4
 
4.0%
Shawn Mendes3
 
3.0%
Billie Eilish3
 
3.0%
Maroon 53
 
3.0%
The Chainsmokers3
 
3.0%
Justin Bieber3
 
3.0%
Travis Scott2
 
2.0%
Other values (54)63
63.0%

Length

2021-10-07T22:40:09.565494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the8
 
4.2%
post7
 
3.7%
malone7
 
3.7%
sheeran5
 
2.6%
ed5
 
2.6%
weeknd4
 
2.1%
54
 
2.1%
imagine4
 
2.1%
justin4
 
2.1%
dragons4
 
2.1%
Other values (103)139
72.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

top genre
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct34
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Memory size928.0 B
dance pop
28 
pop
11 
dfw rap
modern rock
canadian pop
Other values (29)
42 

Length

Max length25
Median length9
Mean length9.87
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)21.0%

Sample

1st rowcanadian contemporary r&b
2nd rowpop
3rd rowcali rap
4th rowpop
5th rowpop

Common Values

ValueCountFrequency (%)
dance pop28
28.0%
pop11
 
11.0%
dfw rap7
 
7.0%
modern rock6
 
6.0%
canadian pop6
 
6.0%
canadian contemporary r&b4
 
4.0%
electropop4
 
4.0%
melodic rap3
 
3.0%
latin2
 
2.0%
folk-pop2
 
2.0%
Other values (24)27
27.0%

Length

2021-10-07T22:40:09.875757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pop50
26.6%
dance30
16.0%
rap18
 
9.6%
canadian12
 
6.4%
rock8
 
4.3%
dfw7
 
3.7%
hop6
 
3.2%
hip6
 
3.2%
modern6
 
3.2%
r&b4
 
2.1%
Other values (29)41
21.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.96
Minimum1975
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2021-10-07T22:40:10.137540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1975
5-th percentile2012
Q12015
median2017
Q32018
95-th percentile2020
Maximum2021
Range46
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.327496806
Coefficient of variation (CV)0.002642659977
Kurtosis37.37937618
Mean2015.96
Median Absolute Deviation (MAD)2
Skewness-5.40386338
Sum201596
Variance28.38222222
MonotonicityNot monotonic
2021-10-07T22:40:10.394554image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
201820
20.0%
201916
16.0%
201616
16.0%
201513
13.0%
201712
12.0%
20146
 
6.0%
20134
 
4.0%
20203
 
3.0%
20213
 
3.0%
20123
 
3.0%
Other values (4)4
 
4.0%
ValueCountFrequency (%)
19751
 
1.0%
19951
 
1.0%
20041
 
1.0%
20081
 
1.0%
20123
 
3.0%
20134
 
4.0%
20146
 
6.0%
201513
13.0%
201616
16.0%
201712
12.0%
ValueCountFrequency (%)
20213
 
3.0%
20203
 
3.0%
201916
16.0%
201820
20.0%
201712
12.0%
201616
16.0%
201513
13.0%
20146
 
6.0%
20134
 
4.0%
20123
 
3.0%

beats.per.minute
Real number (ℝ≥0)

HIGH CORRELATION

Distinct56
Distinct (%)56.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.97
Minimum71
Maximum186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2021-10-07T22:40:10.933593image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum71
5-th percentile79.95
Q195
median115
Q3135.25
95-th percentile171
Maximum186
Range115
Interquartile range (IQR)40.25

Descriptive statistics

Standard deviation27.47062894
Coefficient of variation (CV)0.2348519188
Kurtosis-0.4028637027
Mean116.97
Median Absolute Deviation (MAD)20
Skewness0.5907689298
Sum11697
Variance754.6354545
MonotonicityNot monotonic
2021-10-07T22:40:11.405690image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
906
 
6.0%
1006
 
6.0%
1205
 
5.0%
1364
 
4.0%
953
 
3.0%
1243
 
3.0%
983
 
3.0%
1023
 
3.0%
1253
 
3.0%
1082
 
2.0%
Other values (46)62
62.0%
ValueCountFrequency (%)
711
1.0%
751
1.0%
761
1.0%
771
1.0%
791
1.0%
801
1.0%
832
2.0%
842
2.0%
851
1.0%
891
1.0%
ValueCountFrequency (%)
1861
1.0%
1782
2.0%
1741
1.0%
1712
2.0%
1701
1.0%
1681
1.0%
1602
2.0%
1552
2.0%
1511
1.0%
1502
2.0%

energy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.68
Minimum11
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2021-10-07T22:40:11.855941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile36.8
Q152
median64.5
Q376
95-th percentile85.05
Maximum92
Range81
Interquartile range (IQR)24

Descriptive statistics

Standard deviation16.49173653
Coefficient of variation (CV)0.2631100276
Kurtosis-0.1864652981
Mean62.68
Median Absolute Deviation (MAD)12.5
Skewness-0.5067749133
Sum6268
Variance271.9773737
MonotonicityNot monotonic
2021-10-07T22:40:12.277431image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
736
 
6.0%
565
 
5.0%
804
 
4.0%
544
 
4.0%
614
 
4.0%
523
 
3.0%
823
 
3.0%
593
 
3.0%
793
 
3.0%
783
 
3.0%
Other values (40)62
62.0%
ValueCountFrequency (%)
111
1.0%
261
1.0%
301
1.0%
321
1.0%
331
1.0%
371
1.0%
382
2.0%
392
2.0%
402
2.0%
411
1.0%
ValueCountFrequency (%)
921
 
1.0%
911
 
1.0%
901
 
1.0%
871
 
1.0%
861
 
1.0%
851
 
1.0%
832
2.0%
823
3.0%
811
 
1.0%
804
4.0%

danceability
Real number (ℝ≥0)

HIGH CORRELATION

Distinct46
Distinct (%)46.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.96
Minimum35
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2021-10-07T22:40:12.658981image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile40.85
Q159
median69
Q377
95-th percentile85.05
Maximum91
Range56
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.6040101
Coefficient of variation (CV)0.2031662202
Kurtosis-0.2485413235
Mean66.96
Median Absolute Deviation (MAD)9
Skewness-0.623671297
Sum6696
Variance185.0690909
MonotonicityNot monotonic
2021-10-07T22:40:12.964639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
757
 
7.0%
786
 
6.0%
735
 
5.0%
694
 
4.0%
614
 
4.0%
513
 
3.0%
793
 
3.0%
773
 
3.0%
593
 
3.0%
663
 
3.0%
Other values (36)59
59.0%
ValueCountFrequency (%)
352
2.0%
361
1.0%
371
1.0%
381
1.0%
411
1.0%
422
2.0%
441
1.0%
452
2.0%
481
1.0%
501
1.0%
ValueCountFrequency (%)
911
 
1.0%
901
 
1.0%
881
 
1.0%
871
 
1.0%
861
 
1.0%
853
3.0%
841
 
1.0%
832
2.0%
822
2.0%
801
 
1.0%

loudness.dB
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.1
Minimum-14
Maximum-3
Zeros0
Zeros (%)0.0%
Negative100
Negative (%)100.0%
Memory size928.0 B
2021-10-07T22:40:13.244528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-14
5-th percentile-10
Q1-7
median-6
Q3-5
95-th percentile-3
Maximum-3
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.987333628
Coefficient of variation (CV)-0.3257923981
Kurtosis1.889514027
Mean-6.1
Median Absolute Deviation (MAD)1
Skewness-0.9935568513
Sum-610
Variance3.949494949
MonotonicityNot monotonic
2021-10-07T22:40:13.474539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
-625
25.0%
-520
20.0%
-716
16.0%
-413
13.0%
-89
 
9.0%
-37
 
7.0%
-104
 
4.0%
-93
 
3.0%
-112
 
2.0%
-141
 
1.0%
ValueCountFrequency (%)
-141
 
1.0%
-112
 
2.0%
-104
 
4.0%
-93
 
3.0%
-89
 
9.0%
-716
16.0%
-625
25.0%
-520
20.0%
-413
13.0%
-37
 
7.0%
ValueCountFrequency (%)
-37
 
7.0%
-413
13.0%
-520
20.0%
-625
25.0%
-716
16.0%
-89
 
9.0%
-93
 
3.0%
-104
 
4.0%
-112
 
2.0%
-141
 
1.0%

liveness
Real number (ℝ≥0)

HIGH CORRELATION

Distinct32
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.86
Minimum3
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2021-10-07T22:40:13.735931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile7
Q110
median12
Q317.25
95-th percentile39.05
Maximum79
Range76
Interquartile range (IQR)7.25

Descriptive statistics

Standard deviation12.97240272
Coefficient of variation (CV)0.7694189039
Kurtosis7.248451807
Mean16.86
Median Absolute Deviation (MAD)3
Skewness2.492425144
Sum1686
Variance168.2832323
MonotonicityNot monotonic
2021-10-07T22:40:14.035691image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
913
13.0%
1113
13.0%
1011
 
11.0%
126
 
6.0%
135
 
5.0%
145
 
5.0%
155
 
5.0%
84
 
4.0%
164
 
4.0%
73
 
3.0%
Other values (22)31
31.0%
ValueCountFrequency (%)
31
 
1.0%
51
 
1.0%
62
 
2.0%
73
 
3.0%
84
 
4.0%
913
13.0%
1011
11.0%
1113
13.0%
126
6.0%
135
 
5.0%
ValueCountFrequency (%)
791
1.0%
671
1.0%
561
1.0%
551
1.0%
401
1.0%
391
1.0%
372
2.0%
351
1.0%
342
2.0%
322
2.0%

valance
Real number (ℝ≥0)

HIGH CORRELATION

Distinct58
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.97
Minimum6
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2021-10-07T22:40:14.373401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile15.95
Q133.75
median48
Q366
95-th percentile86.1
Maximum93
Range87
Interquartile range (IQR)32.25

Descriptive statistics

Standard deviation21.7378574
Coefficient of variation (CV)0.4350181589
Kurtosis-0.7642314509
Mean49.97
Median Absolute Deviation (MAD)16
Skewness0.1094557968
Sum4997
Variance472.5344444
MonotonicityNot monotonic
2021-10-07T22:40:14.704957image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
455
 
5.0%
333
 
3.0%
423
 
3.0%
643
 
3.0%
433
 
3.0%
223
 
3.0%
493
 
3.0%
753
 
3.0%
733
 
3.0%
242
 
2.0%
Other values (48)69
69.0%
ValueCountFrequency (%)
61
 
1.0%
121
 
1.0%
131
 
1.0%
141
 
1.0%
151
 
1.0%
161
 
1.0%
172
2.0%
181
 
1.0%
202
2.0%
223
3.0%
ValueCountFrequency (%)
932
2.0%
911
 
1.0%
901
 
1.0%
881
 
1.0%
862
2.0%
851
 
1.0%
842
2.0%
801
 
1.0%
791
 
1.0%
753
3.0%

length
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct67
Distinct (%)67.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214.53
Minimum119
Maximum354
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2021-10-07T22:40:15.024556image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum119
5-th percentile171.75
Q1190.5
median210
Q3234.25
95-th percentile270
Maximum354
Range235
Interquartile range (IQR)43.75

Descriptive statistics

Standard deviation35.93497354
Coefficient of variation (CV)0.1675055868
Kurtosis2.344361334
Mean214.53
Median Absolute Deviation (MAD)23
Skewness0.8176253281
Sum21453
Variance1291.322323
MonotonicityNot monotonic
2021-10-07T22:40:15.400309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2073
 
3.0%
2343
 
3.0%
2423
 
3.0%
2303
 
3.0%
1743
 
3.0%
1773
 
3.0%
2093
 
3.0%
2232
 
2.0%
1962
 
2.0%
2592
 
2.0%
Other values (57)73
73.0%
ValueCountFrequency (%)
1191
 
1.0%
1411
 
1.0%
1571
 
1.0%
1581
 
1.0%
1671
 
1.0%
1721
 
1.0%
1731
 
1.0%
1743
3.0%
1773
3.0%
1781
 
1.0%
ValueCountFrequency (%)
3541
1.0%
3211
1.0%
3131
1.0%
2821
1.0%
2702
2.0%
2631
1.0%
2592
2.0%
2581
1.0%
2571
1.0%
2532
2.0%

acousticness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct50
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.95
Minimum0
Maximum98
Zeros8
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2021-10-07T22:40:15.786433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median13
Q341.5
95-th percentile75.45
Maximum98
Range98
Interquartile range (IQR)37.5

Descriptive statistics

Standard deviation26.2787601
Coefficient of variation (CV)1.053256918
Kurtosis0.03092218018
Mean24.95
Median Absolute Deviation (MAD)12
Skewness1.049044333
Sum2495
Variance690.5732323
MonotonicityNot monotonic
2021-10-07T22:40:16.436561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08
 
8.0%
18
 
8.0%
35
 
5.0%
55
 
5.0%
114
 
4.0%
74
 
4.0%
193
 
3.0%
593
 
3.0%
83
 
3.0%
23
 
3.0%
Other values (40)54
54.0%
ValueCountFrequency (%)
08
8.0%
18
8.0%
23
 
3.0%
35
5.0%
42
 
2.0%
55
5.0%
62
 
2.0%
74
4.0%
83
 
3.0%
92
 
2.0%
ValueCountFrequency (%)
981
1.0%
931
1.0%
921
1.0%
842
2.0%
751
1.0%
701
1.0%
691
1.0%
641
1.0%
631
1.0%
621
1.0%

speechiness
Real number (ℝ≥0)

HIGH CORRELATION

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.93
Minimum2
Maximum46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2021-10-07T22:40:16.774059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q14
median6
Q311
95-th percentile32.05
Maximum46
Range44
Interquartile range (IQR)7

Descriptive statistics

Standard deviation9.424077266
Coefficient of variation (CV)0.9490510842
Kurtosis3.805475159
Mean9.93
Median Absolute Deviation (MAD)2.5
Skewness2.031738877
Sum993
Variance88.81323232
MonotonicityNot monotonic
2021-10-07T22:40:17.042631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
415
15.0%
315
15.0%
515
15.0%
78
 
8.0%
67
 
7.0%
85
 
5.0%
105
 
5.0%
114
 
4.0%
133
 
3.0%
142
 
2.0%
Other values (21)21
21.0%
ValueCountFrequency (%)
21
 
1.0%
315
15.0%
415
15.0%
515
15.0%
67
7.0%
78
8.0%
85
 
5.0%
91
 
1.0%
105
 
5.0%
114
 
4.0%
ValueCountFrequency (%)
461
1.0%
441
1.0%
381
1.0%
341
1.0%
331
1.0%
321
1.0%
291
1.0%
281
1.0%
271
1.0%
251
1.0%

popularity
Real number (ℝ≥0)

HIGH CORRELATION

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.67
Minimum53
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size928.0 B
2021-10-07T22:40:17.302800image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum53
5-th percentile66.95
Q179
median81
Q383
95-th percentile86
Maximum91
Range38
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.905065451
Coefficient of variation (CV)0.07411905926
Kurtosis5.969788398
Mean79.67
Median Absolute Deviation (MAD)2
Skewness-2.040313074
Sum7967
Variance34.86979798
MonotonicityDecreasing
2021-10-07T22:40:17.565959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
8016
16.0%
8213
13.0%
8113
13.0%
8410
10.0%
838
8.0%
798
8.0%
865
 
5.0%
763
 
3.0%
723
 
3.0%
663
 
3.0%
Other values (12)18
18.0%
ValueCountFrequency (%)
531
 
1.0%
561
 
1.0%
663
3.0%
671
 
1.0%
702
2.0%
711
 
1.0%
723
3.0%
741
 
1.0%
751
 
1.0%
763
3.0%
ValueCountFrequency (%)
911
 
1.0%
882
 
2.0%
865
 
5.0%
852
 
2.0%
8410
10.0%
838
8.0%
8213
13.0%
8113
13.0%
8016
16.0%
798
8.0%

Interactions

2021-10-07T22:40:05.034511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:33.290646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:36.368975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:39.857220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:43.953480image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:46.964766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:49.611874image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:53.777614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:56.612398image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:59.559645image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:02.252477image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:05.294679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:33.666378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:36.681855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:40.174491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:44.192276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:47.194345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:49.863509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:54.114243image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:56.837797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:59.773625image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:02.464658image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:05.537977image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:33.861204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:37.252329image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:40.504407image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:44.437372image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:47.433470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:50.119963image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:54.432402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:57.074680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:00.031280image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:02.704548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:05.817496image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:34.152292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:37.608244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:41.005060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:44.984831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:47.693463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:50.394531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:54.755077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:57.324532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:00.317849image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:02.961949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:06.059004image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:34.360535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:37.985021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:41.586560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:45.257148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:47.936547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:50.643628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:54.987991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:57.559643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:00.560093image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:03.192328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:06.322191image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:34.564535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:38.239409image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:42.155262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:45.504478image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:48.173453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:51.114586image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:55.221147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:57.784876image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:00.825518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:03.422333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:06.584560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:34.864697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:38.504592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:42.555486image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:45.764540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:48.426909image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:51.464303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:55.473525image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:58.044591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:01.084610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:03.684497image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:06.822324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:35.080592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:38.744967image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:42.884671image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:45.998399image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:48.657603image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:51.714479image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:55.691338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:58.458740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:01.304627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:03.904646image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:07.085559image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:35.294336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:38.985732image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:43.143474image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:46.233514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:48.885448image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:52.198912image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:55.905341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:58.743531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:01.537241image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:04.125545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:07.344650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:35.584414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:39.240197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:43.404699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:46.464566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:49.123495image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:52.695718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:56.154470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:59.036648image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:01.765512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:04.354526image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:07.591375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:35.925500image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:39.469284image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:43.663862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:46.695244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:49.356937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:53.065390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:56.370950image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:39:59.289137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:01.997122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-07T22:40:04.779722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-10-07T22:40:17.843541image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-07T22:40:18.297591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-07T22:40:18.753584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-07T22:40:19.154517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-10-07T22:40:19.483587image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-07T22:40:08.104440image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-07T22:40:08.712190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

titleartisttop genreyearbeats.per.minuteenergydanceabilityloudness.dBlivenessvalancelengthacousticnessspeechinesspopularity
0Blinding LightsThe Weekndcanadian contemporary r&b20201717351-69332000691
1Watermelon SugarHarry Stylespop2019958255-4345617412588
2Mood (feat. iann dior)24kGoldncali rap2021917270-4327314117488
3Someone You LovedLewis Capaldipop20191104150-6114518275386
4PerfectEd Sheeranpop2017954560-6111726316286
5BelieverImagine Dragonsmodern rock20171257878-486720461386
6lovely (with Khalid)Billie Eilishelectropop20181153035-10101220093386
7CirclesPost Malonedfw rap20191207670-395521519486
8Shape of YouEd Sheeranpop2017966583-399323458885
9MemoriesMaroon 5pop2021913378-786018984685

Last rows

titleartisttop genreyearbeats.per.minuteenergydanceabilityloudness.dBlivenessvalancelengthacousticnessspeechinesspopularity
90CAN'T STOP THE FEELING! (from DreamWorks Animation's "TROLLS")Justin Timberlakedance pop20161138367-610702381772
91Lean OnMajor Lazerdance pop2015988172-356271770671
92Despacito - RemixLuis Fonsilatin20191788065-4786230231870
93Lose YourselfEminemdetroit hip hop20141717469-537632112770
94Without Me (with Juice WRLD)Halseydance pop20191365174-6184522936767
95One DanceDrakecanadian hip hop20161046179-632431741666
96SugarMaroon 5pop20151207975-79882356366
97EmotionsMark Mendypop dance20211268366-5407417252966
98Cold WaterMajor Lazerdance pop2018938061-516501857456
99I Took A Pill In Ibiza - Seeb RemixMike Posnerdance pop20161027367-796619831053